Feature Extraction
Transformers
Safetensors
sentence-transformers
ONNX
English
bert
embeddings
text-embeddings
semantic-search
information-retrieval
int8
minilm
e5
text-embeddings-inference
Instructions to use GrowBitLabs/tinye5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GrowBitLabs/tinye5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GrowBitLabs/tinye5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("GrowBitLabs/tinye5") model = AutoModel.from_pretrained("GrowBitLabs/tinye5", device_map="auto") - sentence-transformers
How to use GrowBitLabs/tinye5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GrowBitLabs/tinye5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Fix model card metadata
Browse files
README.md
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# TinyE5-L6-384
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**TinyE5-L6-384** is a compact **384-dimensional text embedding model** built from
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## Docker Example
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services:
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embedding-server:
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image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.9
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- ./data:/data
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command:
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- --model-id
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- --pooling
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- mean
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```
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```
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## Attribution
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Developed by **[GrowBit Labs](https://growbitlabs.com)**.
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: feature-extraction
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base_model: sentence-transformers/all-MiniLM-L6-v2
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tags:
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- sentence-transformers
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- embeddings
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- text-embeddings
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- semantic-search
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- information-retrieval
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- onnx
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- int8
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- minilm
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- e5
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---
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# TinyE5-L6-384
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**TinyE5-L6-384** is a compact **384-dimensional text embedding model** built from
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## Docker Example
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### INT8 ONNX
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```yaml
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services:
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embedding-server:
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image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.9
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- ./data:/data
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command:
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- --model-id
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- rizwan3d/tinye5
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- --revision
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- int8-onnx
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- --pooling
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- mean
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```
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### Available Variants
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All variants are published under the same Hugging Face repository:
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```text
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rizwan3d/tinye5
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```
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| Variant | Revision | Model |
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| ---------------- | ----------- | ------------------- |
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| Safetensors FP32 | `main` | `model.safetensors` |
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| ONNX FP32 | `fp32-onnx` | `onnx/model.onnx` |
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| ONNX INT8 | `int8-onnx` | `onnx/model.onnx` |
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### Safetensors
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```yaml
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command:
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- --model-id
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- rizwan3d/tinye5
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- --revision
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- main
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- --pooling
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- mean
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```
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### FP32 ONNX
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```yaml
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command:
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- --model-id
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- rizwan3d/tinye5
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- --revision
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- fp32-onnx
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- --pooling
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- mean
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```
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### INT8 ONNX
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```yaml
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command:
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- --model-id
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- rizwan3d/tinye5
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- --revision
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- int8-onnx
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- --pooling
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- mean
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```
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For CPU deployment, the **INT8 ONNX** revision is recommended when model size and inference throughput are the priority.
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## Attribution
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Developed by **[GrowBit Labs](https://growbitlabs.com)**.
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